Note that for the pooled BiLSTM, different encoder layers have different lengths: 2 full length layers, followed by 1 at 1 3 length and 3 at 1 6 length.

we report the average compression across layers here and for the HM in Section 5.2.3.


In return, the generative module can be used to evaluate the quality of extracted skeletons.

we use the feedback of the generative module to reward extracted skeletons.


However, since the style of the sentence compression dataset is very different from that of the narrative story dataset, it is difficult for the pre-trained module to give narrative text accurate compression results.

the supervisory signals are noisy and need to be further improved.


Due to the discrete choice of words in skeletons, the loss is no longer differentiable over the skeleton extraction module.

we use policy gradient (Sutton et al., 1999) to train the skeleton extraction module.


If a skeleton contains too little information, it will get harder for the skeleton-to-sentence component to reconstruct the original sentence based on the skeleton.

the cross-entropy loss of this example will be higher compared with other skeleton-sentence pairs.


Although the quantitative evaluation generally indicates the quality of generated stories, it can not accurately evaluate the generated text.

we also perform a human evaluation on the test set.


Although the proposed model outperforms the state-of-the-art models, it needs to be noted that the highest coherence score, 5.62, is a moderate result in human evaluation, indicating that there is still a long way to go before the generated stories reach the human level.

in this subsection, we give a detailed error analysis to explore what factors affect the performance.


When applying it to generating tasks where only the history context is given, there is no way to explicitly take into account the future information.

reranking-based models do not apply here.


However, the MMI principle is applied only at the testing phase rather than the training phase.

it can only be used to evaluate the quality of a generation by estimating its mutual information with the context.


In contrast, our model maximizes the mutual information in the continuous space and trains the prior distribution through the reparamaterization trick.

our model can be more easily trained with a lower variance.


The input of the neural regression function is a nonlinear transformation of the feature vector produced by EASE, � f .

this model combines the feature vector computed by EASE, � f , and the coherence vector produced by CohEmb, � p, to have a more reliable representation of an essay.


mation from the memoir so that an essay with the highest score has the highest coverage of all relevant and specific information from the memoir.

mentioning the details of the memoir in essays of prompt 5 is more important than coherence for this prompt.


In particular, the bidirectional LSTM is first adopted as a sentence encoder to map the input sentences to the corresponding distributed vectors, and then a self-attention based paragraph encoder is introduced to capture structural relationships across sentences and to obtain a hierarchical context representation of the entire set of sentences.

based on the learned paragraph vector, a pointer network is applied to perform sentence ordering by decoding an ordered sequence.


Given a set of sentences, the existing hierarchical RNN-based models first transform each sentence into a distributed vector with a sentence encoder and then these sentence embeddings are fed to a LSTMs-based paragraph encoder.

based on the learned paragraph vector, a pointer network is exploited to decode the order of the input sentences.


As we see, there is no order information used in the encoding process which prevents the model from being affected by the incorrect sentence order.

self-attention based paragraph encoder is efficient in modeling of dependencies while being invariant to the sentence order.


STEP 1: Our analysis focuses on salient and repeatedly appearing entities in the news.

using position in the lead paragraph as a proxy for salience, we only include entities mentioned at least once in the first three sentences of each article.


We hypothesize that hearer-old entities are referred to by bare names only, and only occasionally by longer REs.

we define acceptance of a hearer-old entity as the time after which its descriptors (the additional words besides the base expression) do not exceed a length of n words on average.


Parameters are, by assumption, independent of each other and switching one parameter entails multiple changes of surface features in general.

by performing phylogenetic inference on the latent space, we can handle the dependencies of all available features in an implicit manner (Step 2).


We do this for each of the 5 samples obtained in Step 1.


Second, the characters of Friends refer to each other explicitly at least once in most conversations and scenes, either using first names or the pronoun you.

we are considering as an interaction most verbal exchanges.


Note that different pairs of people interact more or less in each episode (Table 2).

the classifiers are grounded on general language usage and not modeling who talks and who is talked about.


To our best knowledge, there exists no largescale empirical comparison of different activations across a variety of tasks and network architectures, and even less so within natural language processing (NLP).

1 the question which activation function really performs best and most stably across different NLP tasks and popular NLP models remains unanswered to this date.


All other functions perform considerably worse.

the top-performers are all saturating functions, indicating the different roles activation functions play in LSTMs-those of gates-compared to standard layers.


In contrast to previous tasks, which have a fixed class type, CBT provides different candidate words for each question.

we cannot train it as with a normal classification problem.


We train LSTM-Shuttle to maximize the distribution over a one-hot answer index.

for  1, 10, 1) 46.6% 6.13x * 0.27 Table 5: Cloze result on CBT-NE.


5, all settings of LSTM-Shuttle shuttle more steps after shuttling more times, but LSTM-Jump skims at an almost fixed frequency.

the model reads more words after more shuttles since it tends to read sequential words before each shuttle, in turn yielding better comprehension for the model.


7 is a more difficult task because the answer word "Nora" appears only once in the entire article.


In this study, we assign the mean of keys K to represent the semantic context.

the unified scalar z of a layer is defined as: where Query-Specific Window The last strategy provides a more flexible manner to differentiate the scope by conditioning on each query.


To simplistically visualize how heads cooperate in modeling localness, we propose an additional parametric model which is assigned a learnable but unified window size for each head, namely head-specific.

the window size D m of the m-th head is calculated as: where the scalar z m is a trainable parameter, N = 50 denotes a pre-defined constant number.


Document Analysis, on the other hand, deals largely with problems such as recognizing printed/hand-written characters from a variety of documents (Graves and Schmidhuber, 2009), processing document images for document localization (Javed and Shafait, 2018), binarization (Tensmeyer and Martinez, 2017), and layout segmentation (Chen et al., 2015).

it does not focus on understanding the character-and/or wordlevel semantics of the document the same way as NLP.


We encapsulate all such tasks under the common umbrella of document understanding.

we can formulate this problem as an instance segmentation task of characters on the page.


Line-items can occur in an unknown number of distinct instances.

we require instance segmentation of characters on the document.


Moreover, some character-pixels are misclassified such that the predicted header field above the line-item is incorrect.

we observe errors in both, the segmentation mask and the bounding box predictions.


Rather than selecting the expanded terms within the top-m documents, NPRF uses these documents directly as expansion queries by considering the interactions between them and a target document.

each document's ultimate relevance score depends on both its interactions with the original query and its interactions with these feedback documents.


Additionally, intra-attention is applied in similar fashion as above to generate three more features for each sequence.

the output dimensions for each word becomes d + 6.


Deep networks suffer from inherent difficulty in feature propagation and/or vanishing/exploding gradients.

residual strategies have often been employed Srivastava et al., 2015;Huang et al., 2017).


Each dimension of the positional encoding corresponds to a sinusoid.

the input of our model can be represented as x t = e t + p t .


In the previous work, different approaches conduct experiments on different benchmarks.

we report the average scores on 6 tasks, 7 tasks and 8 tasks, respectively.


A self-attention layer connects all positions with a constant number of sequentially executed operations, whereas a recurrent layer requires O(n) sequential operations.

our model greatly reduces the computational complexity.


This is also consistent with our empirical findings, that the uniform distribution centered by the mean of all word vectors in the documents is generally applicable for various text corpora.

if d is the dimensionality of the pre-trained word embedding space, we can draw a random word u 2 , and where v min and v max are some constants.


G Update As described, updates to function G result in associating a monolingual cluster with a crosslingual cluster (and consequently, other monolingual clusters).

errors committed in updating G are of a higher magnitude than those committed in H, since they involve groups of documents.


Besides, it is able to expand the receptive fields at the exponential level without increasing the number of parameters.

it becomes possible for dilated convolution to capture longer-term dependency.


Moreover, we note that the number of parameters of the hierarchical model is more than that of our model, which are 47.24M and 45.13M respec- tively.

it is obvious that our model does not possess the advantage of parameter number in the comparison.


Neural language models can also be used as contextualized word representations (Peters et al., 2018).

language modeling is a good benchmark task for investigating the general frameworks of neural methods in NLP field.


In other words, if we take a matrix from F (A) and apply the softmax function to each of its rows, we obtain a matrix that consists of true probability distributions.

for some A ∈ F (A), training RNN language models is to find the parameters satisfying the following equation: Equation 6 indicates that training RNN language models can also be interpreted as a matrix factorization problem.


In most cases, the rank of matrix HW is D h N because D h N is smaller than V and U in common RNN language models.

an RNN language model cannot express true distributions if D h N is much smaller than rank(A ).


Formally, instead of Equation 2, DOC computes the output probability distribution at timestep t + 1 by the following equation: where π j,ct is a weight for each probability distribution, k j,ct ∈ R d is a vector computed from each hidden state h n , andW ∈ R V ×d is a weight matrix.

p t+1 is the weighted average of J probability distributions.


Then we obtain matrixÃ ∈ R U ×V : where K j ∈ R U ×d is a matrix whose rows are vector k j,c .Ã can be an arbitrary high rank because the righthand side of Equation 9 computes not only the matrix multiplication but also a nonlinear function.

an RNN language model with DOC can output a distribution matrix whose rank is identical to one of the true distributions.


In addition, Table  3 shows that increasing the distributions from the final layer (i 3 = 20) degraded the score from the language model with i 3 = 15 (the top row of Table 3).

to obtain a superior language model, we should not increase the number of distributions from the final layer; we should instead use the middle layers, as with our proposed DOC.


In contrast, AWD-LSTM-DOC constantly decreased the perplexity and achieved a lower value than the other methods with ordinary SGD.

we conclude that DOC positively affects the training of language modeling.


In the Switchboard training set (Godfrey and Holliman, 1993), over 60% of the words in the reparandum are exact copies of words in the repair.

this similarity is strong evidence of a disfluency that can help the model detect reparanda (Charniak and Johnson, 2001;.


Thus, this similarity is strong evidence of a disfluency that can help the model detect reparanda (Charniak and Johnson, 2001;.

models which are able to detect "rough copies" are likely to perform well on this task.


However, as the autocorrelation kernels contain an additional dimension, both models have a similar number of parameters overall.

both models should have similar learning capacity except for their architec-tural differences (which is what we wish to investigate).


GLTs learn representations at low dimensionality.

a GLT requires g fewer parameters than the linear transformation.


In a linear transformation, each neuron receives an input from each element in the input vector while in a GLT, each neuron receives an input from a subset of the input vector.

gLT is the same as a linear transformation when g = 1.


The path from representation to words in attention are even shorter than the balanced tree.

the fact that attentive pooling outperforms balanced trees on WSR is not surprising to us.


The first part can be formulated as Here we use subscripts to denote layers and superscripts to denote timesteps.

h t l represents the hidden state of the L-th layer at timestep t. x w t 2 R H 0 is the input embedding of word w t where H 0 is the input embedding size.


For simplicity, we ignore the superscript t. We design the Sememe Predictor as a linear decoder with the sigmoid activation function.

q k , the probability that the next word contains sememe e k , is formulated as where v k 2 R H 1 , b k 2 R are trainable parameters, and (•) denotes the sigmoid activation function.


Our model targets at improving the performance and interpretability of language modeling using external knowledge in HowNet.

we take its philosophy of the word-sensesememe hierarchy to design our hierarchical decoder.


Particularly, we propose a Siamese Labeled Topic Model (SLTM) to exploit the information of documents and labels based on the Siamese network (Bromley et al., 1993;Hu et al., 2014;Wang and Zhang, 2017), where weight matrices in SLTM represent conditional distributions.

by constraining weight matrices during the learning procedure, SLTM can follow probabilistic characteristics of topic models strictly.


Given a document d i with labels y i , our goal is to discover topics with a neural network framework.

we first describe the neural network view of topic models briefly.


Labels of d i are y i , which are generated from the topic distribution of d i as: where z k is the indicator vector of z k .

words can be generated from d i as: The architecture of SLTM from the perspective of neural networks is shown in Figure 2.


Our objective is to make word v j be learned by topics in document d i , while not be learned by topics in the negative sampled document d .

we only take word v j in V into consideration during the learning procedure, which can be implemented as dot-multiplying p(V|d i ) with the indicator vector of Particularly, the objective is to make the predicted conditional probabilityp(v j |d i ) approach the observed conditional probability p(v j |d i ) (i.e., term frequency of word v j in document d i ), while make the conditional probabilityp( ) approach zero.


Different from the previous word embedding generation methods, SLTM generates word embeddings based on the global label-specific topic information (i.e., the topical embedding space).

we further compare the quality of word embeddings learned by SLTM and three widely used methods: Word2Vec (W2V) (Mikolov et al., 2013), subword information Word2Vec (siW2V) , and SSPMI (Levy and Goldberg, 2014).


This is because DDTM is globally normalized with edges parameterized as log-bilinear weights, which results in the mean-field updates taking the form of matrix operations followed by nonlinearities.

a single iteration of mean-field is equivalent to a forward pass through a recursive neural network, whose architecture is defined by the tree structure of the thread.


In a sense, we have an exponential number of topics, whose parameterization simply factors over the bits.

it can be difficult to interpret them as one would interpret topics learned by a model such as LDA.


Furthermore, we find that in practice this effect is correlated with the topic embedding size; the more bits our model has, the less sparse and consequently less individually meaningful the bits become.

for this analysis, we specifically focus on DDTM trained with 64 bits total.


This review is unusual in that it studies a single intervention (decision aids) across different populations.

if the model is successful in learning disentangled representations, the corresponding P vectors should roughly cluster, while the I/C should not.


µ S , while taking into account the (pseudo) covariance of S. In binary classification, however, the mean vector µ S is likely to be located near the decision boundary, particularly under a balanced setting.

a small d(x, S) actually implies lower confidence of the corresponding classifier, which is counter-intuitive.


To this end, we instead define the confidence e(x, S) as the difference between the distances from x to each category of S: Here S + and S − stand for the positive space and negative space of S respectively.

if x is either far away from S (i.e., x is not in the manifold of S) or near the classification boundary, we will get a small e(x, S) indicating a low confidence to the corresponding prediction.


MoE objective For each example in each metatarget domain, we compute its MoE posterior using the corresponding meta-sources.

we get the following MoE loss over the entire multi-source training data: Note that α is normalized over the meta-sources for each meta-target, rather than over all the K sources.


During adaptation the model performs a single parameter update after each sentence and does not train until convergence with gradual reduction of the learning rate as would normally be the case during LM training.

the learning 5 For additional details as well as the reverse setting (adaptation to PO), see Supplementary Materials.


It could be considered a mild complement, since the author still endorses renting the movie.

you could argue again that the second example is more difficult than the first.


From the view of human, we should first predict the target label and give corresponding sentiment label afterwards.

we introduce target label information into predicting sentiment label.


With F as input, We utilize another MBi-GRU to learn the hidden states as the final representations of the sentence.

the hierarchical two-layer MBi-GRU architecture can learn highlevel abstract features with consideration of both character-level and word-level information.


Empathy for positive events remains less well understood and is thought to be regulated differently (Morelli et al., 2015).

we focus on empathetic reactions to need or suffering.


In the very beginning of the training, the model's translation capability is nearly zero and the generated sentences are often meaningless and do not contain useful information for the training, so it is unreasonable to directly apply the greedy search strategy.

we first apply the teacher forcing algorithm to pretrain the model, and then we let the model generate the sentences itself and learn from its own outputs.


For example, il and ce can be anaphoric or pleonastic pronouns, but il has a more referential character.

in certain constructions that are strongly pleonastic (e.g.


The articles in Wikipedia also contain anchors linking to each other.

it is convenient to align sentences in Wikipedia articles to KB facts in Wikidata.


Finally, we remove relations with fewer than 1000 instances, and randomly keep 1000 instances for the rest of the relations.

we get a candidate set of 122 relations and 122, 000 instances.


If the two annotators have disagreements on this instance, it will be assigned to a third annotator.

each instance has at least two same annotations, which will be the final decision.


In order to learn the parameters for this model, in each iteration, (Guo et al., 2016) select one of the tasks with some probabilities, sample a mini-batch of examples in the dataset of the chosen task, and update the model parameters using the objective function specific to the chosen task.

the model parameters for feature representation learning are updated at every iteration while only the model parameters in the output layer for the chosen task are updated at the current iteration.


In fact, the performance of the ALT method is even worse than the traditional SEPARATE method also over different network architectures and datasets.

training a single deep learning model on a combination of ED and WSD data (as in ALT) does not automatically enable the model to learn to exploit the similar structures of the two tasks.


For example, pronouns are often linked to entities mentioned in other sentences, while their initial word vectors lack dependency information.

a word representation model cannot learn an informative embedding of a pronoun without considering cross-sentence dependency in this case.


We present an unseen hypernym extraction framework and analyze the pollution problem with this setup.

we argue that only by using unseen candidates in evaluation can truly test whether the model learns the true relation representation, instead of being polluted by the seen training examples.


By repeatedly applying propagation rules, it assigns a relevance score r k of a word vector x (i) .

a relevance score vector r (i,j) is obtained for each word vector x (i) and target dimension y Among a number of propagation rules (Bach et al., 2015), we use the "alpha-beta" rule for multilayer neural networks.


3 Our proposed gender neutralizing scheme is to reserve the gender feature, known as "protected attribute" into w (g) .

the information encoded in w (a) is independent of gender influence.


Unlike Dozat and Manning (2016), our model jointly learns to perform UPOS tagging and parsing, instead of treating them as separate tasks.

instead of tag embeddings, we add a softmax classifier to predict UPOS tags after the first bi-LSTM layer.


Another  pattern that distinguishes the pleonastic use of it is associated with certain sequences of elements such as 'adjective + noun phrase' and 'complementiser + noun phrase' (Evans, 2001).

the linguistic features proposed by Evans (2001) and used in our experiments make possible the utilization of corpus-based knowledge for the automatic classification of it.


are syntactically possible while semantically incorrect 1 .

knowing all the rules of splitting is insufficient and it is essential to identify the location(s) of split(s) in a given compound word.


This gave a very low precision for the tools as shown in Figure 3.

we relaxed this constraint and considered an output to be correct if the true split is present in the top ten predictions of the list.


Unlike English and many other languages, Chinese sentences have no explicit word boundaries.

chinese Word Segmentation (cWS) is a crucial step for many chinese Natural Language Processing (NLP) tasks such as syntactic parsing, information retrieval and word representation learning (Grave et al., 2018).


Similar to neural language modeling, the goal of segmental language modeling is to learn the joint probability function of the segmented sequences of characters.

for each segment, we have: where t is the distributed representation for the t th character in the i th segment and y (1:i−1) is the previous segments.


However, in unsupervised scheme, the given sentences are not segmented.

the probability for SLMs to generate a given sentence is the joint probability of all possible segmentation: where y (i) T i +1 = eos is the end of segment symbol at the end of each segment, and y (i) 0 is the context representation of y (1:i−1) .


It is hard for our generative models to split them apart.

we propose a rulebased post-processing module to deal with this problem, where we explicitly split the attached particles from other words.


While there do exist words that contain more than four characters, most of the Chinese words are unigram or bigram.

k denotes a trade-off between the accuracy of short words and long words.


It can not learn the dependency features in the word sequences and the synonym or near-synonym according to the word meanings.

it performs not so well, which indicates that the new corpus can not be learned by simple surface features and the deep semantic relationsn need to be mined by deep models.


In real-world usage, the input sentences x will be generated by a human user, who associates the capabilities of the parser to a particular topic (e.g., air travel).

most of the x ∈ L \ Φ will share topic with thex ∈ Φ.


Many low-resource languages we deal with in the Experiments section are morphologically rich.

we use a character-level model to address the rare word problem and to learn morphological regularities among words.


Our research interest was to observe which augmentation technique would improve which language, rather than finding one good model.

we have not used development sets to choose one good augmentation model.


Caution with cloze-style RC datasets We note that cloze-style datasets are often created programatically.

it's possible for a dataset to be produced, published, and incorporated into many downstream studies, all without many personhours spent manually inspecting the data.


As human conversation is inherently complex and ambiguous, learning an open-domain conversational AI that can carry on arbitrary tasks is still very far-off (Vinyals and Le, 2015).

instead of focusing on creating ambitious conversational agents that can reach human-level intelligence, industrial practice has focused on building task-oriented dialogue systems  that can help with specific tasks such as flight reservation (Seneff and Polifroni, 2000) ⇤ The work was done while at the University of Cambridge.


This information is persistent across turns and is used to query the database.

the annotation of a belief state is performed implicitly while the wizard is allowed to fully focus on providing the required information.


This setup allowed the dialogue act annotators to also report errors (e.g., not following the task or confusing utterances) found in the collected dialogues.


Even though all dialogues are coherent, some of them were not finished in terms of task description.

the validation and test sets only contain fully successful dialogues thus enabling a fair comparison of models.


For instance, on en → f r, PBSMT tuned on 100 parallel sentences obtains a BLEU score of 26.42 on newstest 2014, compared to 27.09 with default hyper-parameters, and 28.02 when tuned on the 3000 parallel sentences of newstest 2013.

unless otherwise specified, all PBSMT models considered in the paper use default hyperparameter values, and do not use any parallel resource whatsoever.


In our experiments, we found the unsupervised criterion to be highly correlated with the test metric when using the Transformer model, but not always for the LSTM.

unless otherwise specified, we select the best LSTM models using a small validation set of 100 parallel sentences, and the best Transformer models with the unsupervised criterion.


Of course, at inference time y is unknown.

we propose to replace each token y j with a placeholder tokenp.


We implement BISON based on the BERT Pytorch code 1 and initialize with the pre-trained BERT model BERT-BASE-UNCASED (Devlin et al., 2018).

we employ the same model architecture and tokenisation as (Devlin et al., 2018) resulting in a model with about 110M parameters.


We are curious how big the effect of the pre-trained model is.

instead of starting with the BERT-BASE-UNCASED weights, we initialize BISON with random weights drawn from a normal distribution with mean 0.0 and standard deviation of 0.02.


After running a transformer encoder where each position can attend to every other position, a placeholder token will have a probability distribution over the output vocabulary and this distribution is informed by all other tokens in input and output.

a place-  Table 7: Average attention weights and standard deviation when predicting from left-to-right on both SHARC and DAILY DIALOG (DD) for different parts of the sequence, where α 1 is for the input sequence x, α 2 /ᾱ 2 is for the already produced sequence y and α 3 /ᾱ 3 is for the sequence of remaining placeholder tokens p. α k are the normalized attention weights across all three parts, whereasᾱ k normalizes over the second and third part.


Word in previous rows have been produced already, whereas words of later rows still hold placeholder tokens.

the upper triangle of the matrix shows the attention that is paid to future tokens.


However, progress in machine learning is fast.

in many settings, an actively acquired dataset may remain in use (much) longer than the source model used to acquire it.


The application to ABSC is almost straightforward, but is complicated a bit by the decomposition of sentences into fragments: each sentence level decision now corresponds to multiple fragment-level decisions.

we apply the sentence-level (task A) classifier C s on the aspectlevel corpus D t by applying it on the sentence level and then associating the predicted sentence labels with each of the fragments, resulting in Figure 1: Illustration of the algorithm.


In theory, it allows for stronger "self-normalization" during training (Arora et al., 2017), and in practice, subtracting the mean vector from static embeddings leads to improvements on several downstream NLP tasks (Mu et al., 2018).

the extreme degree of anisotropy seen in contextualized word representations -particularly in higher layersis surprising.


Similarly, upper layers of LSTMs trained on NLP tasks learn more task-specific representations (Liu et al., 2019a).

it follows that upper layers of neural language models learn more context-specific representations, so as to predict the next word for a given context more accurately.


Given that isotropy has benefits for static embeddings, it may also have benefits for contextualized word representations, although the latter have already yielded significant improvements despite being highly anisotropic.

adding an anisotropy penalty to the language modelling objective -to encourage the contextualized representations to be more isotropic -may yield even better results.


Quantitatively, the majority of GloVe and fastText vectors fail the Shapiro-Wilk normality test at sig-nificance level 0.05.

while Pearson's r (and thus cosine similarity) may be acceptable for word2vec, it is preferable to resort to more robust non-parametic correlation coefficients such as Spearman's ρ or Kendall's τ as a similarity measure between GloVe and fastText vectors.


(2005a) define the Hilbert-Schmidt independence criterion (HSIC) to be the squared Hilbert-Schmidt norm ||C XY || 2 HS of (2) and derive an expression for it in terms of kernels K and L They also show the empirical estimate of it to be where T is the centering matrix and (j) ), i, j = 1:D are the kernel (Gram) matrices of observations.

crucially, the kernel evaluations for K take place between and not between the individual word embeddings x (i) and x (j) , and similarly for L. both K and L are square matrices of dimension D × D .


With the aid of the likelihood ratio trick, the gradient for the dialog policy is In the fashion of AL, the reward estimator aims to distinguish real human sessions and generated sessions from the dialog policy.

it minimizes KL-divergence with the empirical distribution, while maximizing the KL-divergence with the policy distribution, Similarly, H(p) and H(π θ ) is independent to ω, so the gradient for the reward estimator yields So far, the reward estimation uses the entire session τ , which can be very inefficient because of reward sparsity and may be of high variance due to the different lengths of sessions.


The performance that the agent interacts with VHUS is presented in  it often gives unreasonable responses.

it is more laborious for the dialog policy to learn a proper strategy with the neural user simulator.


Intuitively, the closer the utterance to the response is, the more it reflects the intention of the last dialogue session.

we firstly use the last utterance as key to select context utterances that are relevant to it on the word and sentence level.


However, we find that there are many samples whose last utterance is very short and contains very limited information (such as "good", "ok"), which will cause the selectors to lose too much useful context information.

we propose multi-hop selectors to select more relevant context utterances, yielding k different context.


From the table, we can observe that the one-word noise will bring about 7% ∼ 13% absolute de-crease on R 10 @1 and the three-word noise brings about 20% R 10 @1 decrease.

we can see that matching-based models Zhang et al., 2018;Zhou et al., 2018c;Tao et al., 2019) are very sensitive to small noises of the dataset.


It can not reflect whether key and context are compatible on the overall semantic level.

we continue to evaluate the relevance on the utterance level.


Although "Hop1 Selector" can choose proper context utterances that are related to the last dialogue session, we find that there are many samples whose last utterance contains very little information (such as "good", "ok"), which will cause the selector lose too much useful context information.

we combine it with u i,L−1 , u i,L−2 , ..., u i,L−k by mean pooling.


All the listeners are modeled by a standard transformer decoder layer block, denoted as T RS Dec , which is made of three sub-components: a multi-head self-attention over the response input embedding, a multi-head attention over the output of the emotion tracker, and a position-wise fully connected feed-forward network.

we define the set of listeners as L = [T RS 0 Dec , .


Participants in a conversation are likely to stick to their own emotional state due to their emotional inertia, unless the counterparts invoke a change.

there is always a major interplay between the inter-speaker dependency and selfdependency with respect to the emotional dynamics in the conversation.


By leveraging an external knowledge base, the meaning of "friends" in the forth utterance is enriched by associated knowledge entities, namely "socialize", "party", and "movie".

the implicit "happiness" emotion in the fourth utterance can be inferred more easily via its enriched meaning.


3 The IRER-EA Model Assuming a set of T emotions, L = {l 1 , l 2 , ...l T }, and a set of ed with a list of its relevant emotions R i ⊆ L ranked by their intensities and also a list of irrelevant emotions Relevant emotions and their rankings can be obtained simultaneously according to the scores assigned by the learned ranking function g. The learning objective of relevant emotion ranking (RER) is to both discriminate relevant emotions from irrelevant ones and to rank relevant emotions according to their intensities.

to fulfil the requirements of RER, the global ob-jective function is defined as follows: Here, l s ∈≺ (l t ) represents that emotion l s is less relevant than emotion l t .


they describe verbose experiences or general endorsements) and may not be appropriate to use as explanations in terms of content and language style.

existing models that learn directly from reviews (or tips) may not capture crucial information that explains users' purchases.


As shown in Table 6, both sampling-based methods Ref2Seq (Top-k) and ACMLM achieve higher Distinct-1 and Distinct-2, while their BLEU scores are lower than Seq2Seq based models using beam search.

we also perform human evaluation to validate the generation quality of our proposed methods.


Our hypothesis is that satisfaction polarity can be more or less conveyed by the sentiments of key customer utterances, and meanwhile the sentiments of server utterances are generally polite or non-negative and contain text with context clues which complement the target customer's ut- terances and indirectly affect satisfaction polarity.

we separately denote the customer utterance representations as where M + N = L and L is the total number of utterances in the dialogue.


However, short utterance texts may not contain enough information for semantic representation.

considering context to enhance utterance representation is a natural and reasonable choice.


We opt for an attention mechanism to reward segments that are more likely to be good sentiment predictors.

we measure the importance of each segment representation h ct through a scoring function using feed forward neural network as below: where W u ∈ R k×k , b u ∈ R k and v ∈ R k are trainable parameters, v can be seen as a high-level representation of a fixed query "what is the informative segment" like that used in (Yang et al., 2016).


Finally, we obtain the satisfaction distribution y ∈ R |C| as the weighted sum of sentiment distributions of all the customer utterances by: Note that in the training dataset, our approach only needs the dialogue's satisfaction labels while the utterance sentiment labels are unobserved.

we use the categorical cross-entropy loss to minimize the error between the distribution of the output satisfaction polarity and that of the goldstandard satisfaction label of the dialogue by: where g j i is 1 or 0 indicating whether the i th class is a correct answer for the j th training instance, y j i is the predicted satisfaction probability distribution, and Θ denotes the trainable parameter set.


Server utterances provide helpful context clues which can be defined as sentiment and reasoning clues by the positions of server utterances.

we introduce the position-guided automatic context clue matching mechanism (CCMM) used to match each customer utterance with its most related server utterances, which contain two layers: the position attention layer and the utterance attention layer.


After the bottom-up decoding, we merge the final K-bests by combining identical dependency edges to make the forest.


For fair comparison, we use the same model as the baseline to encode sentences and forests.

our model uses the same number of parameters as our baseline taking 1-best trees.


For any sentence containing several chemical mentions or protein mentions, we keep multiple copies of it with each copy having different target mention pairs.

we only consider the relations of mentions in the same sentence, assigning all cross-sentence chemical-protein pairs as "None" relation.


In particular, the running time of KBESTEISNER with K = 10 is already much longer than that of EDGEWISE.

eDGeWISe better serves our goal compared to KBeSTeISNeR.


The output of RSN p can also be explained as the probability of two sentences mentioning two different relations.

we can use binary labels q and binary cross entropy loss to train our RSN: in which θ indicates all the parameters in the RSN.


Intuitively, when the distance similarity p between two relational instances equals 0.5, there is a high prob-ability that at least one of two instances is near the decision boundary between relation clusters.

we use the conditional entropy loss (Grandvalet and Bengio, 2005), which reaches the maximum when p = 0.5, to penalize close-boundary distribution of data points: Virtual Adversarial Loss.


Moreover, instances in NYT-FB often share entity pairs or relational phrases, which makes it much easier for relation clustering.

we think the results on FewRel-distant are convincing enough for Distantly-supervised OpenRE.


As we can see from Figure 4, RSN is able to roughly distinguish different relations, and Semi-supervised RSN further facilitated knowledge transfer by optimizing the margin between potential relation clusters during training.

semi-supervised RsN can extract more distinguishable novel relations, and gains comparable relational knowledge representation ability with supervised CNN.


In addition to calculating the distance between the two relation vectors, we believe that the more aligned entities exist in the entities that are connected to the two relations, the more likely the two relations are equivalent.

for r 1 and r 2 , we collect the pre-aligned entities existing in the head/tail entities of these two relations as the set P r 1 r 2 = {(e i 1 , e i 2 )|e i 1 ∈ HT r 1 , e i 2 ∈ HT r 2 , (e i 1 , e i 2 ) ∈ L}.


In this work, we further advocate that handling uncommon entities is inevitable when dealing with infrequent relations.

we propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions.


However, abandoning training data may exacerbate the issue of inadequate annotation.

maximizing the potential of massive noisy data as well as highquality part, yet being efficient, is challenging.


(2018) utilized Partial-CRFs (Täckström et al., 2013) to model incomplete annotations for specific domains, but they still rely on seed annotations or a domain dictionary.

we aim at filling the gap in lowresource name tagging research by using only WL  Figure 2: Framework.


During training, they should be regarded as all labels of Y in Partial CRFs, while we sample some of them as O words according to Equation 8.

and and an are instead treated as O words, because they do not appear in any anchor, and are too general due to a high f 3 value.


Note that we have no training instance for O words.

we also use the nonentity sampling (Section 5.1).


When θ n ∈ [0.15, 0.3], the performance decreases greatly due to less available high-quality data for sequence labeling module; meanwhile, little time is saved through classification module.


Top K entities in S t are left to form dynamic context while the others are pruned.

the relevance scores are transformed to attention weights with we can define a weighted coherence score between e j t+1 ∈ E t+1 and S t as where R is a learnable diagonal matrix.


Besides, Kurach also advocates that the gradient penalty (GP) regularization devised in (Gulrajani et al., 2017) will further improve the stability of the model.

the objective function of the proposed AEM is defined as: where L d denotes the discriminator loss, L gp represents the gradient penalty regularization loss, λ is the gradient penalty coefficient which trade-off the two components of objective, ⃗ d * could be obtained by sampling uniformly along a straight line between ⃗ d r and ⃗ d f , P d * denotes the corresponding distribution.


For instance, as shown in Figure 1, although "* is a big city" and "the US Embassy in *" have equal direct extraction quality, the former is considered less useful since its later extracted entities are mostly unrelated.

selecting patterns with high instant feedback but low delayed feedback can cause semantic drift problem (Curran et al., 2007), where the later extracted entities belong to other categories.


To estimate each pattern's importance score, we suggest that a context pattern is important to the semantic of an entity if: the pattern matches the entity frequently; the pattern matches as few other entities as possible.

we design a importance score function for each context pattern p of an entity e as follows: where C(p) is the number of different entities matched by p, N (e, p) is the frequency of p i matching entity e. Ultimately, the above importance scores are mapped to the distributional probabilities of each entity's context pattern such that all probabilities sum up to 1.


Intuitively, given two entities embedded by two ADPEs, they can be regarded similarly to each other, if they have similar context patterns and similar distributions on these patterns.

the similarity measurement should take both context pattern embeddings and corresponding distributional vectors into consideration.


Each head of the encoder-decoder is fed with one type of input sequence, and they are combined at the end of decoder layer.

the tag prediction becomes more stable than using a simple encoderdecoder without the multi-head.


On BioCFE, we observe that 93.8% statement sentences make multiple tuples: 21.7% of the sentences have at least one token that appears in at least one fact tuple and at least one condition tuple, expecting tags "B/I-fYZ" and "B/I-cYZ"; 18.1% of the sentences have at least one token that appears in one condition tuple as a part of subject and in another condition tuple as a part of object, expecting tags "B/I-c1Z" and "B/I-c3Z".

we extend the typical one-output sequence labeling to a multi-output design.


Here we extract only the relation names: Here we take condition sequences as an example to describe the details of the method.

when predicting the j-th tag sequence, we define the position embedding of the i-th token as follows, representing the relative position to the j-th relation name's tag "B-c2p": the tag probability distributions of the i-th token in the condition tag sequences are: Similarly, we have the following tag distributions for the i-th token in the fact tag sequences: where v f i,j is the position embedding of the i-th token in the j-th fact sequence, representing the relative position to the relation name's tag "B-f2p".


Note that in TACRED, each instance contains only a single ground truth label.

the tendency of nEM to predict more labels leads to the reduced precision.


However, the vanilla EE required the trigger words that were absent on factual knowledge bases.

employed either linguistic resources or predefined dictionaries for trigger-words labeling.


To address the arguments-scattering challenge efficiently, it is indispensable to leverage global contexts to better identify whether an entity plays a specific event role.

we utilize document-level entity encoding to encode extracted entity mentions with such contexts and produce an embedding of size d w for each entity mention with a distinct surface name.


Similarly, for each word w, the senses could be sen (w i ) ∈ S (w) .

we can obtain the embeddings of senses by jointly learning word and sense embeddings via Skip-gram manner.


Current cell state c records all historical information flow up to the current time.

the character-based LSTM functions are: where h c i is the hidden state vector.


On the contrary, the TLNN can dynamically select word sense for polysemous triggers by utilizing context information.

the correct event type TransferMoney is predicted.


Both entities contain the same token "Human".

the token should have two different categorical labels.


Despite their shortcomings, layered sequence labeling model and exhaustive region classification model are complementary to each other.

we can combine them to improve the performance of nested NER.


Their tags include both entity and relation information.

they don't need to maintain a entity-relation table.


Our model reads in a raw sentence and generates triplets one by one.

all triplets in a sentence could be extracted.


In time step t and t%3 = 1, the model (policy) is required to determine the relation of the triplet; In time step t where t%3 = 2 or 0, the model is required to determine the first or second entity, which is copied from the source sentence.

the action space A is varied in different time step t. A = R, t%3 = 1 P, t%3 = 2, 0 where R is the predefined relations and P is the positions of source sentence.


But the second triplet of A is different from B.

there are only 1 triplet is in the same position for A and B.


For example, we could add the BIO tag prediction in the encoder and train the BIO loss together with current loss.

we can recognize the complete entity with the help of BIO tags.


In case, values for these arguments are not explicitly mentioned, they take the default values.

caRe(B=convE) represents the same network as caRe(B=convE, PN=Bi-GRU, cN=LAN).


For this experiment, we selected seven RPs, and for each RP, through human judgement, we selected two more RPs with similar meaning.


The worst case is that all sentences in a bag are mislabelled.

one primary challenge in DSRE is to minimize the noisy labelling effects, which in turn, would let model learn from incorrect labelled datasets.


As shown in the column of "piecewise pooling" in Figure 3(a), the grey dots iñ c, representing head and tail entities, split each vector into three pieces, which are denoted as C 1 ,C 2 ,C 3 , respectively.

the piece-wise max pooling is expressed as .


To investigate multilayer effects of DSRE, we present multilayer CNN with ATT results in Table 2, we observe that with ATT several multilayer models (e.g., 2 layers CNN+ATT) have improvements compared with single layer models, and overfitting occurs when larger convolution layers are employed.

our SelfAtt design is expected to boost a sentence encoder with attention scores and alleviate noise effects by benefiting from a multilayer CNN network.


Since 2-hop DS bag S T is collected indirectly through anchor entity pairs in Web tables, despite that it brings abundant information, it also contains a massive amount of noise.

treating S T equally as S may mislead the prediction, especially when their sizes are extremely imbalanced.


We further sample positive samples from (1) in an attempt to prevent the 'not' token from being biased towards negative samples.

for negative samples we get from (1), we change the relationship from affirmation to negation as in (2) to get positive samples.


While other domains have shown progress on event extraction using neural methods (Nguyen and Grishman, 2015;Nguyen et al., 2016;Feng et al., 2016), recent progress in the temporal relation domain is focused more on the setting where gold events are provided.

we first show the performance of a neural event extractor on this task, although it is not our main contribution.


Although the structured joint model is designed to predict events and relations jointly, we leverage the event module to filter out tokens with scores lower than a threshold.

some true events can be mistakenly predicted as non-events, and the relation pairs including them are automatically assigned NONE.


The cumulative reward from current time step to the end of an episode would cancel the intermediate rewards and thus reflect whether the current action improves the holistic label assignment or not.

the learned policy would not focus on the current placement but have a long-term view that takes following actions into account.


It has been demonstrated that multiple senses of a word actually reside in linear superposition within the word embedding so that specific senses can be extracted from the original word embedding.

we propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses.


Compared with the standard neural nets using a single scalar (the output of a neural unit) to represent the detected semantics, the vectorized rep-resentation in capsule network enables us to utilize hyperplanes to extract the component from the overall representation and get the specific sense.

we propose to attach different hyperplanes to capsules to tackle the ambiguity caused by polysemy.


For text classification, each label contains textual information.

similar to the document word, one label can be represented as an embedding vector and the label set will be encoded by a trainable matrix C ∈ R l×k .


α j and β j indicate the importances of self-attention and label-attention to construct the final document representation along the j-th label respectively.

we add the constraint on them: Then, we can obtain the final document representation along the j-th label based on fusion weights as follows.


In real applications, more precious information can be collected, such as label description, la-bel topology (e.g., hierarchical structure) and etc.

it is interesting to extend the current model with such extra information.


The importance of different words to the meanings of an instance is unequal, thus it is worth pointing out which words are useful and which words are useless.

we apply an attention mechanism (Yang et al., 2016) to get those important words and assemble them to compose a more informative instance vector s j , and the definitions are as follows s where h j t is the t hidden word embedding of instance x j , it was encoded through the instance encoder, and has the same hidden size with x j .


BERT also requires special processing due to the length constraint; please refer to the supplementary material for details.

we focus on presenting LSTM with attention in the main paper for ease of understanding.


LIME generates post-hoc explanations by fitting a local sparse linear model to approximate model predictions.


The hidden state h t is computed iteratively based on h t 1 and x t .

the all previous information can be encoded.


We observe that the labels are nearly uniformly distributed in each latent variable value, suggesting that the latent variables are not obviously being used to encode information about the label.

we hypothesize there should be information other than that pertaining to the label that causes the data to cluster into different latent variable values.


Conversely (2), if the representations supporting language understanding in the brain are not linearly decodable from fMRI, we should be able to demonstrate this fact by showing specific ablations of sentence representation models do not degrade in brain decoding performance.

the brain decoding framework offers possibilities both for (1) discriminating among NLU tasks as faithful characterizations of human language understanding, and for (2) understanding potential limitations of fMRI imaging and linear decoding methods.


The once iconic and attractive pack of 13 letters that was placed at the Yankee stadium in 1976 and later removed in 2008 was unexpectedly not favorably considered at the Sotheby's auction when the 68 year old owner of the letters attempted to transfer its ownership to a member the younger populace.

when the minimum estimate of $300,000 was not met, a further attempt was made by a former player of the Yankees to personally visit the new owner as an Table 1: Example summaries collected from human annotators in the constrained (left) and unconstrained (right) task.


The emergence of neural methods changed this: provided there is enough training data, it does become possible to learn a direct mapping from input to output, as has also been shown in, for example, neural machine translation.

in NLG more recently, neural end-to-end data-totext models have been proposed, which directly learn input-output mappings and rely much less on explicit intermediate representations (Wen et al., 2015;Dušek and Jurcicek, 2016;Mei et al., 2016;Lebret et al., 2016;Gehrmann et al., 2018).


ZAP obtains a low F1 score of 56.03 (42.65 precision, 81.7 recall).

xL-BERT shows much better, and more precise results compared to this baseline and achieves overall very acceptable and stable labeling quality.


But with the AUTO trees, DEPPATH&RELPATH is much worse.

structural information is much more sensitive to the quality of dependency trees due to error propagation.


(2019)'s best DEPPATH approach as INPUT(DEPPATH).

we can compare with their work as fairly as possible.


Another drawback of VerbNet is its organization into Levin's classes (Levin, 1993), namely, 329 groups of verbs sharing the same syntactic behavior, independently of their meaning.

its classes cannot be used straightforwardly in a semantic task.


This can be achieved by leveraging BabelNet (Navigli and Ponzetto, 2010), a lexical-semantic resource that provides multilingual synsets in 284 different languages linked to WordNet itself.

verbAtlas can be used in virtually any language, in contrast to Prop-Bank and other resources which are inherently language-specific and require considerable human intervention in each new language.


Therefore, we define the significance of w i in its context to be: It turns out ↵ s can be rewritten as and we use the fact that V i is an orthogonal matrix and q i is orthogonal to all but the last column of S i , v w i .

↵ s is essentially the distance between w i and the context hyper-plane, normalized by the context size.


Pictures and text in social media usually complement each other.

even if the information of interest can be understood from either communication modality, considering both is beneficial.


The performance gain ∆ between C g +P g and T are observed when the visual concretenessc is high: for Captions (c = 3.10), the improvement is substantial: (∆ = +43); for benchmarks with a lower concreteness (News with c = 2.61 and Forum withc = 2.39), the improvement is smaller (∆ = +12).

grounding brings useful complementary information, especially for concrete sentences.


Second, supervised IE systems require the target relations to be predetermined and learn to extract only the predefined relations.

they miss new and potentially meaningful domain relations that are prominent in a given dataset.


Stanford Open IE, OpenIE 5 and UKG can complement each other when combined together.

by ensembling these IE systems, our proposed system SenseOIE achieves better and larger coverage of all possible relation extractions.


A correct fact with an incorrect justification is considered invalid.

reproducing TAC's evaluation for new systems is challenging.


The remaining sentences are ignored during annotation and evaluation.

our annotation framework allows incremental annotation of new properties and is better suited for the goal of annotating 100,000 facts for 100 properties.


The alignment probability a(i|j, l, m) cannot be learned during training because of the absence of bilingual corpus.

cross-lingual BPE embeddings are leveraged to calculate the normalized sim(x i , y j ) to approximate a(i|j, m, l).


It seems that pre-training decoders has a smaller effect on the final performance than pre-training encoders (Lample and Conneau, 2019), one reason for which could be that the encoder-to-decoder attention is not pre-trained.

the parameters of the decoder need to be re-adjusted substantially in the following tuning process for MT task.


Our proposed method can give the model more explicit and strong cross-lingual training signals so that the pre-trained model contains much beneficial cross-lingual information for unsupervised machine translation.

we can further improve the translation performance significantly, compared with Lample and Conneau (2019) (with the significance level of p<0.01).


According to our intuition, directly using the pre-trained model as the decoder may not work well because parameters of the decoder need substantial adjustment due to the attention part between the encoder and the decoder.

we treat the pre-trained decoder as the feature extractor and add several Transformer layers with the encoder-to-decoder attention on top of it.


Although the first one has proven to be beneficial during pre-training to some NLP tasks, we want to check how much its influence is to our final translation performance.

we disable those two modifications in CMLM one by one and report the translation results.


However, the cross-lingual information is obtained mostly from the shared BPE space during their pre-training method, which is inexplicit and limited.

we figure out a new pre-training method that gives the model more explicit and stronger cross-lingual information.


Typically, the first stage decoder produces a beam of latent states, which serve as conditions to feed into the second stage decoder to obtain the target words.

training and inference in these models can only explore a limited sub-space of the latent states.


ten restricted to government documents or news articles.

improving NMT under small-data training conditions has attracted extensive attention in recent years (Sennrich et al., 2016a;Cheng et al., 2016;Zoph et al., 2016;Chen et al., 2017;Fadaee et al., 2017;Ren et al., 2018;Lample et al., 2018).


Translation errors can be propagated to subsequent steps and impair the performance of back-translation, especially whenD b is much larger than D b (Pinnis et al., 2017;Fadaee and Monz, 2018;Poncelas et al., 2018).

it is crucial to develop principled solutions to enable back-translation to better deal with the error propagation problem.


It can be proved that the variance of a probability is no greater than the corresponding expectation.


One potential issue is that the Enc-Dec needs to be able to compress all the necessary information of the input source sentence into context vectors which are fixed during decoding.

a natural question was raised, Will carefully designed aggregation operations help the Enc-Dec paradigm to achieve the best performance?


We first introduce a bi-directional LSTM (BiL-STM) as the primary-capsule layer to incorporate forward and backward context information of the input sentence: Here we produce the sentence-level encoding h t of word x t by concatenating the forward − → h t and backward output vector ← − h t .

the output of BiLSTM encoder are a sequence of vectors H corresponding to the input sequence.


127: When an output capsule c t j receives the incoming messages u t i→j , its state will be updated and the coefficient α t ij is also re-computed for the input capsule h t−1 i .

we iteratively refine the route of information flowing, towards an instance dependent and context aware encoding of a sequence.


(2014) and Ebisu and Ichise (2018) pointed out: 1) TransE and its variants neglect some mapping properties of relations; and 2) TransE and its variants force entity embeddings to be on a sphere in the embedding space, which conflicts with the expectation of TransE and warps embeddings obtained by TransE.

transE and its variants don't do well in modeling multi-mapping relations, such as 1-N, N-1 and N-N relations.


For MTransE, it develops five variants in its alignment model, where the fourth obtains the best results according to the experiments of its authors.

we choose this variant to represent MTransE.


This task is usually done offline as a pre-processing step.

the training time complexity would not be a barrier to this approach.


If we only use domain-specific data to train the NMT model for such a domain, the performance of resulting model is usually unsatisfying.

nMT for low-resource domains becomes a challenge in its research and applications.


The iteration number K is a crucial hyperparameter that directly determines the amount of the transferred translation knowledge under our framework.

we first inspected its impacts on the development sets.


However, alignments are contextsensitive: the decision to align source word i to target word j may affect the probability of aligning the neighbors of i and j.

before applying the sigmoid function to the matrix A we convolve it with a shared weight matrix, yielding a matrix A , which encodes the relationships between alignments; this is given by A = W conv * A, where * is the convolution operator.


Discourse information have proven useful for predicting antecedents, which may occur in previous sentences (Zhao and Ng, 2007;Chen and Ng, 2013).

we further improve ZP prediction with discourse-level context, which is learned together with the joint model.


Encoding Discourse-Level Context Hierarchical structure networks are usually used for modelling discourse context on various natural language processing tasks such query suggestion (Sordoni et al., 2015), dialogue modeling (Serban et al., 2016) and MT (Wang et al., 2017).

we employ hierarchical encoder (Wang et al., 2017) to encoder discourse-level context for NMT.


At decoding time, ZPs can not be annotated by alignment method since target sentences are not available.

source sentences are annotated by an external ZP prediction model, which is trained on monolingual training instancesx.


This also confirms our hypothesis that discourse-level context benefits ZP prediction more than ZP translation.

we incorporate the discourse-level context into reconstructor instead of the decoder.


However, as shown in "Non-Fixed Error" case, there are still some ZPs can not be precisely predicted due to the misunderstanding of intentions of utterances.

exploiting dialogue focus for ZP translation is our future work (Rao et al., 2015).


The functionality relies on a fast iterative process called routing-byagreement, whose basic idea is to iteratively refine the proportion of how much a part should be assigned to a whole, based on the agreement between the part and the whole .

it is appealing to investigate if this mechanism could be employed for our intuition.


For example, function words in English (e.g., "the") could not find its counterpart translation in Chinese.

we add additional Redundant Capsules Ω R (also known as "orphan capsules" in Sabour et al.


Bilingual Content Agreement Intuitively, the translated source contents should be semantically equivalent to the translated target contents, and so do untranslated contents.

a natural idea is to encourage the source PAST contents, modeled by the PAST capsule to be close to the target PAST representation at each decoding step, and the same for the FUTURE.


While this error detection problem can be modeled as sequential labelling task, we choose simple classification model as the benchmark.

we use an Mutilayer Perceptron (MLP) as the detection model which takes all above features in for each source word s i to choose one of the labels in {OK, W, WT, M, MT} as the prediction.


8), the mis-prediction is caused by the failure of bilingual alignment (between chengban in S and in charge in R).

our model regards chengban as omittable as bilingual alignment fails to align it to in charge in R. though chengban is translated in R but not translated in T , we classify it as oK instead of M. This example again demonstrates the important of alignment in error detection task.


Instead, the word importance strongly correlates with the POS tags and fertility features, and these features in total contribute over 95%.

in the following analyses, we mainly focus on the POS tags (Table 3) and fertility properties (Table 4).


Frequent word forms can often be found in humancurated dictionaries.

the practical purpose of training a BLI model should be to create transla-   (Conneau et al., 2018), which was generated using an "internal translation tool", and in which the majority of word pairs consist of forms ranked in the top 10k of the vocabularies of their respective languages.


For highly inflectional languages, many of the infrequent types are rare forms of otherwise common lexemes and, given the morphological regularity of less frequent forms, a model that generalizes well should be able to translate those capably.

to gain insight into the models' generalization ability we first examine the relation between their performance and the frequency of words in the test set.


In this setting, our method is equal to Tiedemann and Agić (2016) where the only difference the our baseline parsing model.

our method can be regarded as a generalization of both source-side training  and fully translated target training (Tiedemann and Agić, 2016) with fine-grained control over translation confidence.


The two words "are" and "being" both have no aligned words in the other side, and meanwhile "are" has a lower retention score compared with "being".

2 the source word "are" is prefer to be deleted.


order of the machine-translated sentence.

we adjust the leaf nodes, letting the word order strictly follow the machine-translated sentence order For example, the word "vi" is moved from the first position into the third position, and similarly the word "mu" is moved from the last position into the first position.


In contrast, in our model we consider the decoder state when the sibling was first generated from its parent.

this state contains the sibling's parent information, which helps with capturing long term dependencies.


(2018) used UD Treebanks 2.1, which is not the most up-to-date version.

during experiments, we rerun their codes with UD Treebanks 2.3 to match our experiments.


We find that only sharing Transformer layers is not enough for forcing hidden representations from different languages into a common space, as suggested in experiments (Section 3.4).

we propose three methods to build crosslingual representations based on identical strings, mean/variance, and average linkage.


As Chinese and English do not share the alphabet, the number of identical strings is significantly smaller than similar languages pairs such as English-Spanish.

the MLMA-Iden achieves a lower result comparing to MUSE which uses adversarial training.


4 POS mainly relies on the information of each single word, and parallel corpora providing word alignment are effective for cross-lingual POS.

previous annotation projection methods through parallel corpora are strong approaches for cross-lingual POS and often achieve a significantly better performance against previous unsupervised methods.


Model transfer methods train a model on the source language with language-independent features.

the trained model can be directly applied to the target language.


However, these methods are usually based on RNN or CRF to sequentially encode a sentence, while the underlying structure of language is not strictly sequential (Shen et al., 2019).

these models would encounter serious word ambiguity problems (Mich et al., 2000).


Global Relay Node Update In terms of the global state g, recent works (Zhang et al., 2018b;Guo et al., 2019) have shown the effectiveness of sharing useful messages across contexts.

we also designed an update function for g, with the initialization g 0 = average(C, E).


Moreover, different from the lattice LSTM, which also leverages lexicon features, our LGN model integrates lexicon information into the graph neural network in a more effective fashion.

it outperforms the lattice LSTM on all three datasets.


The inherent nature of BiLSTMs is able to supplement global sequential information, which is insufficiently modeled in the previous local calculation layer.

we apply an additional BiLSTMs layer upon the local calculation layer in each CM-block.


In addition, the global information in S-LSTM is modeled by aggregating the local features with gating mechanisms, which may lose sight of sequential information of the whole sentence.

we apply external BiLSTMs to supply global sequential features, which is shown highly necessary for both tasks in our experiments.


However, a supervised parser may be unavailable when the language is low-resourced or the target data has different distribution from the source domain.

the task of learning latent tree structures without human-annotated data, called grammar induction (Carroll and Charniak, 1992;Klein and Manning, 2002;Smith and Eisner, 2005), has become an important problem and attractd more attention from researchers recently.


To make each position not attend to the position in different constituents, Tree Transformer constrains the attention probability matrix E by constituent prior C as below, where is the element-wise multiplication.

if C i,j has small value, it indicates that the positions i and j belong to different constituents, where the attention weight E i,j would be small.


That is, giving both s i,i+1 and s i,i−1 large values, so the attention head freely attends to any position without restriction of constituent prior, which is the same as the original Transformer.

the softmax function is to constraint the attention to be sparse.


All values of a on top few layers are very close to 1, suggesting that those are not good breakpoints.

we set a threshold for deciding a breakpoint, where a minimum a will be viewed as a valid breakpoint only if its value is below the threshold.


Prior work discovered that the representations from the lower layers of Transformer are not informative .

using syntactic structures from lower layers decreases the quality of parse trees.


In each masked testing data, only one word is replaced with a "[MASK]" token.

each sentence creates the number of testing samples equal to its 3 The perplexity of masked words is e − log(p) n mask , where p is the probability of correct masked word to be predicted and n mask is the total number of masked words.


To obtain the refined role distribution, we add up M α and I α that we got from the baseline model, and follow that by a softmax layer: To build a representation for sense disambiguation, we simply compute the probability mass for each role (excluding the null role) to obtain r π , and concatenate this with g π and Π • P t : Differently from the role refinement network, sense prediction is predicate-specific.

we first map z π to R dπ , and then take the inner product with the predicate-specific sense embeddings Π to get the refined logits: Similarly to role refinement, σ is the logistic function, W π ∈ R dr×(r−1+dg+dπ) , W π ∈ R m×dr are learned linear mappings.


We suspect that a significant portion of this degradation is due to distribution shift (as evidenced by improvement in Hindi F 1 in Parallel regime).

one promising route to improving our models might be to incorporate domain adaptation techniques, which aim to build classifiers robust to various forms of distribution shift.


To keep our scenarios as realistic as possible, we assume that no taggers are available since this would entail substantial annotation.

our neural parsing models must learn to parse from words or characters-that is, they must be lexicalized-even though there may be little shared vocabulary between source and target treebanks.


On the other hand, grammar parsers are not robust to the flexibility of language because of their use of string matching.

we propose a method that preserves the robustness of neural semantic parsers while addressing the data sparsity of the one-shot semantic parsing task.


This is a challenging task for a complex grammar since the domain of a typical grammar consists of hundreds of thousand of examples.

being able to parse all variations of utterances in the domain by seeing only a single example from it does not have a trivial solution.


Using existing semantic parsing datasets to directly evaluate one-shot parsing is difficult, because evaluation of one-shot parsing requires grouping of utterances by structural similarity to construct domains.

we generate our own data 1 based on a synchronous context free grammar.


The grammar we use do not constrain the type of the set command.

some sentences generated are not semantically correct according to a human interpreter but still they preserve the sentence structure.


Unlike conventional classifiers, SAC uses vectors to indicate tags.

sAC can calculate the similarities between words and tags, and then compute a weighted sum of the tag vectors, which can be considered a useful feature for NER tasks.


For instance, if the j th word is an entity word, its feature representation h c j must have a higher similarity score with v 1 .

sAC operates in a manner that is similar to a "supervised cluster," separating entity words from non-entity words, and denoting two "clustering centers" with two tag vectors.


However, for SAC, an accuracy score of 97.87% also means that 97.87% of   the words share common traits in which a majority of entity words are similar to the "True" tag vector, while the majority of non-entity words are similar to "False" tag vector.

because of the higher F1 score obtained from NeuralCRF+SAC, the two tag vectors can be viewed as features that are helpful for the NER task.


In fact, many long entities exhibit a pattern in which almost all words are written in uppercase letters except one.

it is common that the segmentation problem grows in severity as entities become longer.


When the kernel sizes are set to 1, 2 and 3, CNN layers can extract uni-gram, bi-gram and tri-gram features that enable SAC to learn a considerable number of samples that exhibit the same structure as "Time of London."

sAC reduces truncation errors for long entities caused by lowercase words.


Crucially, the proposed method is naturally not sensitive to problems of lexical biases faced by standard beam search.

the parser is framed as generative and strictly incremental (without lookahead) while remaining quite accurate and generally more efficient than a more traditional beam search method.


However direct generative parsing with RNNG is difficult because word generation transitions have significantly lower probabilities than structural transitions, such as open and close transitions.

inside a beam at the same temporal step, these lexical transitions are likely to be pruned.


We briefly summarize in the following how this is achieved through the computation of marginal probabilities.

we can define the probability of a prefix as: computing transition probabilities can be simply performed as: The naive computation of marginal probabilities stated in (5) requires to process a set Y(x i ) growing exponentially in size with the length of the input sequence.


only for single annotators might be misleading, because specific annotators vary in the difficulty of examples they produce.

running a model on a new annotator that produces easy examples will not result in decreased performance.


Hence, our goal is to learn a single classifier that can adapt to the output of any selector operating at any budget.

this classifier must be robust so that it can achieve consistent performance with different budgets.


As a matter of fact, for multiple budgets, none of the skim-RNN, and LSTM-jump address the prob-  lem of different word distribution between training and testing.

similar to skim-RNN, we anticipate that the behavior of LSTM-jump will be inconsistent as well 8 .


This might be the result of the tuning process of the sparse rational structure simply learning a collection of words, rather than coherant phrases.

this WFSA is treated as a unigram pattern rather than a trigram.


Densifier is based on solving an optimization problem using stochastic gradient descent with iterative orthogonalization of Q. Den-sRay, in contrast, is an analytical solution.

we expect DensRay to be more robust, which is confirmed by our experiments.


Assuming we normalize the vectors d vw one can interpret Eq.

1 as follows: we search for a unit vector q such that the square of the cosine similar- we identify dimensions that are parallel/orthogonal to difference vectors of words belonging to different/same classes.


We use a slightly different notation in this section: for a word w the ith component of its embedding is given by E w,i .

we denote as E •,1 the first column of E (i.e., the most interpretable dimension).


In this paper, we investigate three ways to perform MetaUpdate as described in the following parts.

mAmL The vanilla mAmL algorithm (Finn et al., 2017) updates the model with the metaobjective function: mAmL would implement the metaUpdate step by updating θ according to: where β is a hyper-parameter.


In our preliminary experiments, we observed that SGD optimizer is more stable and less likely to quickly overfit the training set than other optimizers with adaptive learning rates (Reddi et al., 2018; Kingma and Ba, 2015).

we use SGD with the learning rate of 0.005 and a batch size of 128.


At the same time, because f is invertible the average number of bits that can be encoded is given by the entropy H(q).

the compression objective is to maximize H(q).


However, when it comes to longer documents, basic attention mechanism may lead to distraction and fail to attend to the relatively salient parts.

some works focus on designing various attentions to tackle this issue (Tan et al., 2017;Gehrmann et al., 2018).


To make a more correct decision on what to write next, people always adjust the concentrated content by reconsidering the current state of what has been summarized already.

aRU is designed as an update unit based on current decoding state, aiming to retain the attention on salient parts but weaken the attention on irrelevant parts of input.


However, as previous work (Huang et al., 1979;Jung et al., 2018) mentioned that the median value is more robust to outliers than the mean value, we use the median value to calculate the variance of the attention distribution.

local variance loss can be calculated as: where• is a median operator and is utilized to avoid zero in the denominator.


Wikipedia and SimpleWiki have similar domains, but different grammatical complexities.

we can investigate how grammatical complexity affects model performance by comparing these two corpora.


(2015) provide a blueprint for Gibbs sampling with potential functions without additional parameters, our model has additional parameters of ρ S→T and ρ T →S so we need to optimize them.


As shown in Table 4, the additional language model only introduces 23.6% more training time, since it does not involve expensive attention computation.

nLG-LM can offer more natural response generation with comparable efficiency.


With different settings of parameter α, Dirichlet distribution may have various forms, which can be concave or convex, monotonously rising or decreasing, symmetrical or asymmetrical.

equipped with the diverse structure, Dirichlet distribution is capable of modeling the complex latent variable.


Embedding metrics first map the generating responses to a vector space and then compute the cosine similarly with corresponding ground responses.

it can be used to measure the sentence-level semantic similarity to a certain extent.


We also conduct Friedman test (Friedman, 1940) on the human evaluation score to check its statistical significance, and the value of Chi-square test statistics is 5696.25 which indicates that the P-value is less than 0.05.

dir-VHREd is significantly better than others.


In our above experiments, we find an interesting phenomenon for Dir-VHRED that there are many non-active dimensions of z and the number of active ones is almost always around 3.

we conduct additional ablation study experiments to analyze each dimension of z.


we can easily generate the responses with the preferred style and length through changing the value of each dimension of z.


In ProgModel, each model M t has an independent g t which is initialized by g 0 in M 0 .

α α α t i will be fine tuned from α α α 0 i during training M t via updated g t and t i .


Thanks to the capability of knowledge transfer in ProgModel, M t can already remember quite much previously learned knowledge to give good label prediction in many cases.

we consider two types of decision engines: (1) t-IDE: ProgModel using only the output of M t as decision engine; (2) c-IDE: for i th word, it combines all outputs from is an indicator function which is 1 when i th word is in the vocabulary of M k and 0 otherwise.


The values in pink show that the performance of FT-AttRNN and FT-Cp-AttRNN drops up to 3.82% and 5.38% respectively.

their F1 scores are significantly reduced in the end.


On the other hand, FT-Lr-AttRNN has difficulty to learn new knowledge since it cannot jump out of local optimum due to a small learning rate.

the performance of FT-Lr-AttRNN is even lower than FT-AttRNN most of the time.


With more and more training data at each batch, the increase of ProgModel size is significantly slower than that of FT-Cp-AttRNN since the capability of knowledge transfer in ProgModel avoids the full model copy.

our approach also better trades off the model size and performance.


Such a training set might contain many false negatives and trivial true negatives that are very easy to distinguish from those true positives.

models with advanced architectures can only reach sub-optimal performance after learning .


On the other hand, we do see improvement on the two data sets.

we believe it is worth paying a little more training time but obtaining the improvement.


We then add Gaussian noise (Zheng et al., 2016) to further compensate the imperfect alignment of cross-lingual embeddings.

a combination of these methods allows us to build a transferable latent variable model that learns the distribution of training language inputs that is invariant to noise in the cross-  lingual word embeddings.


The objective functions for slot filling and intent prediction are: hence, the final objective function to minimize is, The model prediction is not deterministic since the latent variables z S t and z I are sampled from the Gaussian distributions.

in the inference time, we use the true mean µ S t and µ I to replace z S t and z I respectively to make the prediction deterministic.


We discretize the Probability of applying an operation into 10 uniformly-spaced values starting from 0.1 and let the number of replaces range from 1 to 4.

the search space size is (12 × 4 × 10) (2×4) = 2.82 × 10 21 .


For example, modern Chinese text may allow characters such as: traditional and simplified Chinese characters, special punctuation marks, fullwidth characters, emojis, mathematical symbols, Latin characters, currency symbols, scientific notations, etc.

scripting robust rules often requires a considerable amount of time and effort.


As shown in Table 1  In MT pipelines, language models are often used for corpus filtering, fusion techniques and N -best reranking.

we perform LM experiments on MT corpora.


3 The length of the refinement path to search is at most N + M (all deletions/insertions), so refining the path requires at most (N + M )w comparisons.

the full N M comparisons can be approximated by (N + M )w + N 2 M 2 comparisons.


A sequence of such actions for a sentence pair ( , ) defines one way to translate into .

such a sequence must have | | number of WRITE actions.


fore all WRITE actions usually has large latency.

the ideal sequences for simultaneous translation should have the following two properties: • there is no anticipation during translation, i.e.


However, different scenarios may have different latency requirements.

this greedy policy may not always be the best choice for all situations.


For each step in translation, we choose READ action only if the probability of READ is greater than ; otherwise we choose WRITE action.

this threshold balances the tradeoff between latency and translation quality: with larger , the policy prefers to take WRITE actions, providing lower latency; and with smaller , the policy prefers to take READ actions, and provides more conservative translation with larger latency.


That is, any word in the entire vocabulary has the same PE on the same position index.

the dependencies encoded by the original PEs are independent of word content, which may further hinder the improvement of translation capacity.


Specifically, the embedding of each word x j is divided into two parts x p j and x r j , and their dimensions are respectively.

the sequence of word vectors An RNN with an nonlinear projection layer is then designed to learn its recurrent state r j for each word over where W r ∈ R dr×dr is a parameter matrix and b r ∈ R dr is a bias item.


All heads in pr j are further concatenated as a mixed embedding m j ∈ R d model in turn.

there is a new sequence of mixed embeddings: The M is mapped to a query matrix Q M , and their keys and values matrices K M and V M .


In contrast, the encoder would not be expected to contain this mechanism.

the position information would be more important in the encoder than the decoder.


Collecting large amounts of parallel data in all possible domains we are interested in is costly, and in many cases impossible.

it is essential to explore effective methods to train models that generalize well to new domains.


Note that given a sentence, a graph-based dependency parser has three levels of representations: the model parameters, the scores of dependency edges computed from the parameters, and the parse tree computed from the edge scores.

we propose three different regularization terms to effectively encourage similarity of the two models.


However, concatenating forward and backward hidden states doubles the dimension.


Korean morphological analysis has been considered as a sequence of morpheme processing and POS tagging.

a pipeline model of the tasks has been adopted widely by previous studies.


Korean is an agglutinative language (Song, 2006).

it is a fundamental step for understanding a sentence to analyze the grammatical structure of eojeols, where an eojeol is a linguistic unit segmented by a white space.


An eojeol is composed of one or more morphemes.

one eojeol can be analyzed into several morpheme combinations depending on a context, which yields different part-of-speech (POS) tags of the morpheme combinations.


That is, the first '나는' is decomposed into '나 (I)' and '는 (topical postposition)', while the second '나는' is into '날 (fly)' and '는 (verbal ending)'.

morpheme processing and POS tagging should be trained simultaneously.


Note that syllable is the unit for the model and one POS tag spans several syllables of a morpheme.

to identify morpheme boundaries, morpheme processing of the proposed model generates a special symbol of morpheme ending for every explicit morpheme boundary instead of adopting the IOB tagging scheme.


When training the 'Tied' multi-task model, the feedbacks from POS tagging network influence decoder states directly.

the 'Tied' model is able to learn better representation for both morpheme processing and POS tagging.


This paper has formulated Korean morphological analysis as a combination of morpheme processing and POS tagging.

the two tasks are trained simultaneously through the tied sequenceto-sequence multi-task model with the pointergenerator network and the CRF network.


Most languages, especially those that are severely impacted by diacritics, rarely have such amounts of diacritized datasets.

we sample a moderate size subset of the training data for Vietnamese, roughly 3.7% to train the models.


/ wuSuwla and / wuSuwlu, both mean "arrival" but have different syntactic positions), and named entities.

8 incorporating information from longer history in TCN architectures such as A-TCN compared to recurrent models such BiL-STM did not enhance learning inflectional diacritics.


The DER were 2.8% and 3.0% respectively.

a-TCN was 24% faster than BiLSTM while being marginally lower by 0.2% in terms of DER.


Both agents are rewarded for Q-BOT's image-guessing performance and trained with REINFORCE (Williams, 1992) to optimize this reward.

there is incentive for Q-BOT to ask questions informative of the hidden image, and for A-BOT to provide meaningful answers.


2e): Diverse-Q-bot + A-bot (SL and RL) significantly outperform the baseline on percentile rank of ground-truth image as retrieved using Q-BOT's fc7 prediction.

the question-repetition penalty leads to a more informative communication protocol.


In other words, the original context contains a large amount of extra information.

the higher this metric is, the less extra information the caption contains.


For the second challenge, we propose a stochastic sampling scheme to balance teacher-forcing and student-forcing during training, so that the agent can recover from its own mistakes at inference time.

of combining both techniques, on the R2R benchmark test set, our agent (PRESS) 3 achieves 53% on SPL, an absolute 6% gain over the current state of the art.


Given instruction x, the language encoder proceeds in two steps, end-to-end, by considering a function decomposition f θ E = f θx→e • f θe→z :  into a single joint feature Previous methods in VLN learn e either from pretrained word embeddings (Pennington et al., 2014) which do not take into account word context, or from scratch.

their representations do not capture contextual information within each instruction.


First, we compare PRESS to a baseline seq2seq model 5 in two evaluation settings on the validation splits: (1) S: A single instruction is provided to the agent at a time.

three separate navigation trajectories are generated corresponding to three alternative instructions in this setting.


To do so, we rely on standard bidirectional long shortterm memory networks (BILSTMs) (Hochreiter and Schmidhuber, 1997;Schuster and Paliwal, 1997).

we denote LSTM θ (w) as the abstraction of a long short-term memory network, that processes an input sentence w=[w 1 , ..., w n ] to generate an output of hidden representations a BILSTM can be seen as BILSTM In this work we will stack two layers of BILSTMs before decoding the output.


(2018) expand the UD scheme to train a parser for Twitter data.

we take a similar approach in expanding the UD scheme to encompass issues common to automated speech transcription.


Many conversational agents are turn-based (Kepuska and Bohouta, 2018) , and depend on pauses in user speech to detect the end of the user's dialog turn.

hesitations by the user can result in the system prematurely terminating the user's turn.


We found that the growth of Meteor and BLEU scores are opposite.

the choice of utilizing how many historical sentences is a trade-off between both scores.


QCN is the crucial part of our overall architecture and the positive and negative impact depends on the configuration of the QCN.

we also investigate the effect of the hyper-parameter of QCN: the number of feature capsules.


As the state-of-the-art Transformer model is welldesigned in the model structure, an analysis of the integration in Transformer is thus necessary.

we perform experiments on Chinese-English document-level translation for analyzing.


First, the answers to the questions involve a wide range of types such as numbers, dates, or text strings.

various kinds of prediction strategies are required to successfully find the answers.


Nevertheless, the prediction of each signed number is isolated, and the expression's context information has not been considered.

obviously-wrong expressions, such as all predicted signs are either minus or zero, are likely produced.


Then we compute the probabilities of logical negation on the i-th number as: Although existing reading comprehension tasks focus exclusively on finding one span of text as the final answer, DROP loosens the restriction so that the answer to the question may be several text spans.

specific adaption should be made to extend previous single-span extraction to multi-span scenario.


A large number of cQA forums thus do not contain enough labeled data for supervised training of neural models.

2 recent works have used alternative training methods.


For instance, they are only partially redundant but fundamentally describe the same question (see Figure 1 for an example).

we can use the information from titles and bodies together with their relations to train our models.


In WS-TB, we thus train models to predict whether a given title and body are related, i.e., whether they belong to the same question.


We see that the model performance consistently improves when we increase the training data (we observe similar trends for DQG and WS-QA).

it is crucial to make use of all available data from the cQA forums.


Interestingly, we do not observe large differences between supervised training and WS-TB for both models when they use the same number of positive training instances (ranging from 2.8k to 5.8k).

using title-body information instead of question-answer pairs to train models without direct answer supervision is feasible and effective.


For these ambiguous or confusing questions, it is hard to directly give satisfactory responses unless systems can ask clarification questions to confirm the participant's intention.

this work explores how to use clarification to improve current KBQA systems.


Recall that we restricted the choice of source domains to the 12 CQADupStack forums.

some target forums were paired with non-ideal source forums (e.g., en-glish→buddhism).


By modeling this as a binary classification problem, it becomes possible to quantify how characteristic of a writer a tweet is, as a probability implied by its distance from the decision boundary.

we obtain a characterization score between 0 and 1 for each tweet.


The negative examples consist of all tweets written by other Twitter users, a total of approximately 500 million per day (https://business.twitter.com).

there is an extreme class imbalance between user and non-user tweets.


In addition, we compute the characterization score of tweet T with respect to user U , as described in Equation 1.

the shorter the length of the encoded tweet, the more characteristic of the user T is.


Indeed, as Figure 1 suggests, current popular tools for toxic language detection do not recognize the toxicity of MAS and further, sentiment tools can label these comments as being positive.

applications using such techniques to promote inoffensive content may potentially promote MAS.


This work is proposed to be deployed in a real-world system.

we suggest to decouple the two modules (i.e., the selector and the predictor).


The evolution of social media users' behavior over time complicates user-level comparison tasks such as verification, classification, clustering, and ranking.

naïve approaches may fail to generalize to new users or even to future observations of previously known users.


Each action consists of all available information from a given platform detailing what exactly the user did, which for purposes of this work we take to include: (1) a timestamp recording when the action occurred, from which we extract a tuple t of temporal features, (2) unstructured text content x of the action, and (3) a categorical feature r specifying the context of the action.

an action is a tuple of the form (t, x, r).


However, when large amounts of data are available, it is preferable to use a data-driven approach to representation learning.

we define f θ using a neural network as follows.


On the other hand, methods similar to the proposed model could be developed for such purposes and not shared with the broader community.

as part of our effort to encourage positive applications, we release source code to reproduce our key results.